Descriptions

In this work we propose a curve approximation method that operates in the curvature
domain. The curvature is represented using one of several different types of
basis functions (linear, quadratic, spline, sinusoidal, orthogonal polynomial), and the
curve's geometry is reconstructed from that curvature basis. Our hypothesis is that
different curvature bases will result in different aesthetics for the reconstructed curve.
We conducted a user study comparing multiple curvature bases, both for aesthetics
and similarity to the original curve, and found statistically significant differences in
how people ranked the reconstructed curve's aesthetics and similarity. To support
adaptive curve fitting we developed a fitting algorithm that matches the original
curve's geometry and explicitly accounts for corners. We also extend this algorithm
to 3D.
Key Words: curvature, aesthetics